TLDR-462 -- test_scan_paragraph_classifier_extractor finally works

*now it uses gpu: before, for 1 line it automatically marked it like a non_paragraph

tests/unit_tests/test_on_gpu.py

@@ -1,6 +1,7 @@
 import os
 
 import cv2
+import pandas as pd
 from dedocutils.data_structures import BBox
 
 from dedoc.data_structures.line_metadata import LineMetadata
@@ -24,9 +25,11 @@ def test_line_type_classifier(self) -> None:
         """
         law_extractor = LawStructureExtractor(config=self.config)
 
-        lines = [LineWithMeta("     З А К О Н"), LineWithMeta("\n"), LineWithMeta("     ГОРОДА МОСКВЫ")]
+        lines = [LineWithMeta("     З А К О Н", metadata=LineMetadata(page_id=0, line_id=0)),
+                 LineWithMeta("\n", metadata=LineMetadata(page_id=0, line_id=1)),
+                 LineWithMeta("     ГОРОДА МОСКВЫ", metadata=LineMetadata(page_id=0, line_id=2))]
+        features = law_extractor.classifier.feature_extractor.transform([lines])
         predictions = law_extractor.classifier.predict(lines)
-
         self.assertListEqual(predictions, ["header", "header", "cellar"])
 
     def test_orientation_classifier(self) -> None:
@@ -48,14 +51,22 @@ def test_txtlayer_classifier(self) -> None:
     def test_scan_paragraph_classifier_extractor(self) -> None:
         classify_lines_with_location = ScanParagraphClassifierExtractor(config=self.config)
         metadata = LineMetadata(page_id=1, line_id=1)
-
+        metadata2 = LineMetadata(page_id=1, line_id=2)
         bbox = BBox(x_top_left=0, y_top_left=0, width=100, height=20)
+        bbox2 = BBox(x_top_left=50, y_top_left=50, width=100, height=20)
         location = Location(page_number=1, bbox=bbox)
-        lines = [LineWithLocation(line="Example line", metadata=metadata, annotations=[], location=location)]
+        location2 = Location(page_number=1, bbox=bbox2)
+        lines = [LineWithLocation(line="Example line", metadata=metadata, annotations=[], location=location),
+                 LineWithLocation(line="Example line 2", metadata=metadata2, annotations=[], location=location2)]
         data = classify_lines_with_location.feature_extractor.transform([lines])
+
         if any((data[col].isna().all() for col in data.columns)):
             labels = ["not_paragraph"] * len(lines)
         else:
             labels = classify_lines_with_location.classifier.predict(data)
 
-        self.assertEqual(labels, ["not_paragraph", "not_paragraph", "not_paragraph"])
+        self.assertEqual(labels[0], "paragraph")
+        self.assertEqual(labels[1], "paragraph")
+
+
+